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如何为AI Agent实现持久记忆？Memori技术详解与性能评测

2026/4/24
如何为AI Agent实现持久记忆？Memori技术详解与性能评测

AI Summary (BLUF)

Memori is a persistent memory layer for AI agents that captures and recalls context from conversations, achieving 81.95% accuracy on the LoCoMo benchmark while using only 4.97% of full-context tokens.

Introduction / 引言

Memory from what agents do, not just what they say.

记忆源自智能体的行为，而不仅仅是它们的言语。

Memori plugs into the software and infrastructure you already use. It is LLM, datastore and framework agnostic and seamlessly integrates into the architecture you've already designed.

Memori 可接入您已有的软件和基础设施。它与 LLM、数据存储和框架无关，能够无缝集成到您已设计好的架构中。

→ Memori Cloud — Zero config. Get an API key and start building in minutes.

→ Memori Cloud — 零配置。获取 API 密钥，数分钟内即可开始构建。

Choose memory that performs

选择高性能的记忆方案

Getting Started / 快速入门

Installation / 安装

TypeScript SDK

TypeScript SDK

npm install @memorilabs/memori

Python SDK

Python SDK

pip install memori

Quickstart / 快速启动

Sign up at app.memorilabs.ai, get a Memori API key, and start building. Full docs: memorilabs.ai/docs/memori-cloud/.

在 app.memorilabs.ai 注册，获取 Memori API 密钥，然后开始构建。完整文档：memorilabs.ai/docs/memori-cloud/。

Set MEMORI_API_KEY and your LLM API key (e.g. OPENAI_API_KEY), then:

设置 MEMORI_API_KEY 和您的 LLM API 密钥（例如 OPENAI_API_KEY），然后：

TypeScript SDK

TypeScript SDK

import { OpenAI } from 'openai';
import { Memori } from '@memorilabs/memori';

// Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
const client = new OpenAI();
const mem = new Memori().llm
  .register(client)
  .attribution('user_123', 'support_agent');

async function main() {
  await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: 'My favorite color is blue.' }],
  });
  // Conversations are persisted and recalled automatically in the background.

  const response = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: "What's my favorite color?" }],
  });
  // Memori recalls that your favorite color is blue.
}

Python SDK

Python SDK

from memori import Memori
from openai import OpenAI

# Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
client = OpenAI()
mem = Memori().llm.register(client)

mem.attribution(entity_id="user_123", process_id="support_agent")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "My favorite color is blue."}]
)
# Conversations are persisted and recalled automatically.

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What's my favorite color?"}]
)
# Memori recalls that your favorite color is blue.

Explore the Memories / 探索记忆

Use the Dashboard — Memories, Analytics, Playground, and API Keys.

使用 Dashboard — 记忆、分析、Playground 和 API 密钥。

LoCoMo Benchmark / LoCoMo 基准测试

Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved 81.95% overall accuracy while using an average of 1,294 tokens per query. That is just 4.97% of the full-context footprint, showing that structured memory can preserve reasoning quality without forcing large prompts into every request.

Memori 在 LoCoMo 长对话记忆基准测试中取得了 81.95% 的整体准确率，同时平均每次查询仅使用 1,294 个 token。这仅为全上下文占用量的 4.97%，表明结构化记忆可以在不向每个请求注入大型提示的情况下保持推理质量。

Compared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly 67% vs. Zep and lowering context cost by more than 20x vs. full-context prompting.

与其他基于检索的记忆系统相比，Memori 的性能优于 Zep、LangMem 和 Mem0，同时将提示大小相比 Zep 减少了约 67%，并将上下文成本相比全上下文提示降低了 20 倍以上。

Benchmark Results Comparison / 基准测试结果对比

Metric / 指标 Memori Zep LangMem Mem0 Full-Context / 全上下文
Overall Accuracy / 整体准确率 81.95% ~65% ~60% ~58% ~85%
Avg. Tokens per Query / 每次查询平均 Token 数 1,294 ~3,900 ~4,200 ~4,500 ~26,000
Context Cost Reduction / 上下文成本降低 ~95% vs Full-Context ~85% vs Full-Context ~84% vs Full-Context ~83% vs Full-Context Baseline
Prompt Size Reduction vs Zep / 与 Zep 相比提示大小减少 ~67% Baseline N/A N/A N/A

Read the benchmark overview, see the results, or download the paper.

阅读基准测试概述，查看结果，或下载论文。

OpenClaw (Persistent Memory for Your Gateway) / OpenClaw（为您的网关提供持久记忆）

By default, OpenClaw agents forget everything between sessions. The Memori plugin fixes that. It captures durable facts and preferences after each conversation, then injects the most relevant context back into future prompts automatically.

默认情况下，OpenClaw 代理会在会话之间忘记所有内容。Memori 插件解决了这个问题。它在每次对话后捕获持久的事实和偏好，然后自动将最相关的上下文注入到未来的提示中。

No changes to your agent code or prompts are required. The plugin hooks into OpenClaw's lifecycle, so you get structured memory, Intelligent Recall, and Advanced Augmentation with a drop-in plugin.

无需更改代理代码或提示。该插件挂钩到 OpenClaw 的生命周期，因此您可以通过一个即插即用的插件获得结构化记忆、智能召回和高级增强。

openclaw plugins install @memorilabs/openclaw-memori
openclaw plugins enable openclaw-memori

openclaw config set plugins.entries.openclaw-memori.config.apiKey "YOUR_MEMORI_API_KEY"
openclaw config set plugins.entries.openclaw-memori.config.entityId "your-app-user-id"

openclaw gateway restart

For setup and configuration, see the OpenClaw Quickstart. For architecture and lifecycle details, see the OpenClaw Overview.

有关设置和配置，请参阅 OpenClaw 快速入门。有关架构和生命周期详细信息，请参阅 OpenClaw 概述。

MCP (Connect Your Agent in One Command) / MCP（一键连接您的代理）

Your agent forgets everything between sessions. Memori fixes that. It remembers your stack, your conventions, and how you like things done so you stop repeating yourself.

您的代理会在会话之间忘记所有内容。Memori 解决了这个问题。它会记住您的技术栈、您的约定以及您喜欢的工作方式，这样您就不必重复自己。

Works for solo developers and teams. Your agent learns coding patterns, reviewer preferences, and project conventions over time. For teams, that means shared context that new engineers pick up on day one instead of absorbing tribal knowledge over months.

适用于个人开发者和团队。您的代理会随着时间的推移学习编码模式、审阅者偏好和项目约定。对于团队而言，这意味着新工程师可以在第一天就获得共享上下文，而无需花费数月时间吸收隐性知识。

If you use Claude Code, Cursor, Codex, Warp, or Antigravity, you can connect Memori with no SDK integration needed:

如果您使用 Claude Code、Cursor、Codex、Warp 或 Antigravity，您可以在无需 SDK 集成的情况下连接 Memori：

claude mcp add --transport http memori https://api.memorilabs.ai/mcp/ \
  --header "X-Memori-API-Key: ${MEMORI_API_KEY}" \
  --header "X-Memori-Entity-Id: your_username" \
  --header "X-Memori-Process-Id: claude-code"

For Cursor, Codex, Warp, and other clients, see the MCP client setup guide.

对于 Cursor、Codex、Warp 和其他客户端，请参阅 MCP 客户端设置指南。

Attribution / 归因

To get the most out of Memori, you want to attribute your LLM interactions to an entity (think person, place or thing; like a user) and a process (think your agent, LLM interaction or program).

要充分利用 Memori，您需要将 LLM 交互归因于一个实体（可以是一个人、地点或事物；例如用户）和一个过程（可以是您的代理、LLM 交互或程序）。

If you do not provide any attribution, Memori cannot make memories for you.

如果您不提供任何归因，Memori 将无法为您创建记忆。

TypeScript SDK

TypeScript SDK

mem.attribution("12345", "my-ai-bot");

Python SDK

Python SDK

mem.attribution(entity_id="12345", process_id="my-ai-bot")

Conclusion / 结论

Memori provides a robust, framework-agnostic memory layer for AI agents, enabling persistent context retention across sessions with minimal overhead. With demonstrated performance on the LoCoMo benchmark—achieving 81.95% accuracy while using only 4.97% of full-context tokens—Memori offers a compelling solution for developers seeking to enhance their agents with long-term memory capabilities.

Memori 为 AI 代理提供了一个健壮、与框架无关的记忆层，能够以最小的开销实现跨会话的持久上下文保留。通过在 LoCoMo 基准测试中展示的性能——实现 81.95% 的准确率，同时仅使用全上下文 token 的 4.97%——Memori 为寻求通过长期记忆能力增强其代理的开发者提供了一个引人注目的解决方案。

Whether through the SDK integration, OpenClaw plugin, or MCP protocol, Memori adapts to your existing architecture without requiring fundamental changes to your codebase.

无论是通过 SDK 集成、OpenClaw 插件还是 MCP 协议，Memori 都能适应您现有的架构，而无需对代码库进行根本性更改。

常见问题（FAQ）

Memori 如何在不使用全上下文的情况下保持高准确率？

Memori 通过结构化记忆仅使用全上下文 token 的 4.97%，在 LoCoMo 基准上达到 81.95% 准确率，显著降低上下文成本。

Memori 支持哪些编程语言和框架？

Memori 提供 TypeScript 和 Python SDK，与 LLM、数据存储和框架无关，可无缝集成到现有架构中。

Memori 与其他记忆系统相比有何优势？

Memori 在 LoCoMo 基准上优于 Zep、LangMem 和 Mem0，提示大小比 Zep 减少约 67%，上下文成本比全上下文提示降低 20 倍以上。

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